Fetching the paper…
Reading the bibliography…
This paper focuses on spectrum sharing in heterogeneous wireless networks, where nodes with different Media Access Control (MAC) protocols to transmit data packets to a common access point over a shared wireless channel.
R. K. Jain, D.-M. W. Chiu, W. R. Hawe et al. , “A quantitative measure of fairness and discrimination,” Eastern Res. Lab., Digit. Equip. Corp., Hudson, MA, USA, Tech. Rep. , vol. 21, 1984
1984
Earlier work this paper cites.
H. Hasselt, “Double Q-learning,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 23, 2010
2010
Earlier work this paper cites.
S. E. Yüksel, J. N. Wilson, and P. D. Gader, “Twenty years of mixture of experts,” IEEE Trans. Neural Networks Learn. Syst. (TNNLS) , vol. 23, no. 8, pp. 1177–1193, 2012
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, Feb. 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , 2016, pp. 770–778
2016
Earlier work this paper cites.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,” in Proc. Int. Conf. Mach. Learn. (ICML) . PMLR, Jul. 2018, pp. 1861–1870
2018
Earlier work this paper cites.
P. Tilghman, “Will rule the airwaves: A DARPA grand challenge seeks autonomous radios to manage the wireless spectrum,” IEEE Spectrum , vol. 56, no. 6, pp. 28–33, Jun. 2019
2019
Earlier work this paper cites.
Y. Yu, T. Wang, and S. C. Liew, “Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks,” IEEE J. Sel. Areas Commun , vol. 37, no. 6, pp. 1277–1290, Jun. 2019
2019
Earlier work this paper cites.
J. Tan, L. Zhang, Y.-C. Liang, and D. Niyato, “Deep Reinforcement Learning for the Coexistence of LAA-LTE and WiFi Systems,” in Proc. IEEE Int. Conf. on Commun. (ICC) , May 2019, pp. 1–6
2019
Earlier work this paper cites.
K. Rakelly, A. Zhou, C. Finn, S. Levine, and D. Quillen, “Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables,” in Proc. Int. Conf. Mach. Learn. (ICML) . PMLR, May 2019, pp. 5331–5340
2019
Cited alongside, same era.
2019
Cited alongside, same era.
X. Ye, Y. Yu, and L. Fu, “Deep Reinforcement Learning Based MAC Protocol for Underwater Acoustic Networks,” IEEE Trans. Mobile Comput. , vol. 21, no. 5, pp. 1625–1638, May 2020
2020
Cited alongside, same era.
Y. Yu, S. C. Liew, and T. Wang, “Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks,” IEEE Trans. Mobile Comput. , vol. 20, no. 9, pp. 2848–2861, Sep. 2021
2021
Cited alongside, same era.
L. Lu, X. Gong, B. Ai, N. Wang, and W. Chen, “Deep Reinforcement Learning for Multiple Access in Dynamic IoT Networks Using Bi-GRU,” in Proc. IEEE Int. Conf. on Commun. (ICC) , May 2022, pp. 3196–3201
2022
Later among the works it cites.
Z. Guo, Z. Chen, P. Liu, J. Luo, X. Yang, and X. Sun, “Multi-Agent Reinforcement Learning-Based Distributed Channel Access for Next Generation Wireless Networks,” IEEE J. Sel. Areas Commun , vol. 40, no. 5, pp. 1587–1599, May 2022
2022
Later among the works it cites.
J. Xiao, H. Xu, X. Sun, F. Luo, and W. Zhan, “Maximum Throughput in the Unlicensed Band under 3GPP Fairness,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC) . IEEE, Aug. 2022, pp. 798–803
2022
Later among the works it cites.
M. A. Jadoon, A. Pastore, M. Navarro, and F. Perez-Cruz, “Deep Reinforcement Learning for Random Access in Machine-Type Communication,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC) , Apr. 2022, pp. 2553–2558
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Retyk, “On Meta-Reinforcement Learning in task distributions with varying dynamics,” Master’s thesis, Universitat Politècnica de Catalunya, Apr. 2021
2021
Cited alongside, same era.
X. Ye, Y. Yu, and L. Fu, “Multi-Channel Opportunistic Access for Heterogeneous Networks Based on Deep Reinforcement Learning,” IEEE Trans. Wireless Commun. , vol. 21, no. 2, pp. 794–807, Feb. 2022
2022
Cited alongside, same era.
H. Xu, X. Sun, H. H. Yang, Z. Guo, P. Liu, and T. Q. S. Quek, “Fair Coexistence in Unlicensed Band for Next Generation Multiple Access: The Art of Learning,” in Proc. IEEE Int. Conf. on Commun. (ICC) , May 2022, pp. 2132–2137
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Y. Yu, S. C. Liew, and T. Wang, “Multi-Agent Deep Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks With Imperfect Channels,” IEEE Trans. Mobile Comput. , vol. 21, no. 10, pp. 3718–3730, Oct. 2022
2022
Cited alongside, same era.
“DARPA SC2 Website,” https://spectrumcollaborationchallenge.com/
Cited in the paper.
Z. Liu, X. Wang, Y. Zhang, and X. Chen, “Meta Reinforcement Learning for Generalized Multiple Access in Heterogeneous Wireless Networks,” in Proc. IEEE Wiopt Workshop Mach. Learn. Wireless Commun. (WMLC) , Aug. 2023, pp. 570–577
2023
Later among the works it cites.
X. Geng and Y. R. Zheng, “Exploiting Propagation Delay in Underwater Acoustic Communication Networks via Deep Reinforcement Learning,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 34, no. 12, pp. 10 626–10 637, 2023
2023
Later among the works it cites.
F. Frommel, G. Capdehourat, and F. Larroca, “Reinforcement Learning Based Coexistence in Mixed 802.11ax and Legacy WLANs,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC) , Mar. 2023, pp. 1–6
2023
Later among the works it cites.
E. Pei, Y. Huang, L. Zhang, Y. Li, and J. Zhang, “Intelligent Access to Unlicensed Spectrum: A Mean Field based Deep Reinforcement Learning Approach,” IEEE Trans. Wireless Commun. , vol. 22, no. 4, pp. 2325–2337, Apr. 2023
2023
Later among the works it cites.
M. Han, Z. Chen, and X. Sun, “Multiple access via curriculum multitask happo based on dynamic heterogeneous wireless network,” IEEE Internet Things J. , vol. 11, no. 21, pp. 35 073–35 085, 2024
2024
Closest in time.